Projected random forests and conformal prediction of circular data

[2410.24145] Projected random forests and conformal prediction of circular data Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Machine Learning arXiv:2410.24145 (stat) [Submitted on 31 Oct 2024 ( v1 ), last revised 9 Jun 2026 (this version, v3)] Title: Projected random forests and conformal prediction of circular data Authors: Paulo C. Marques F. , Rinaldo Artes , Helton Graziadei View a PDF of the paper titled Projected random forests and conformal prediction of circular data, by Paulo C. Marques F. and 1 other authors View PDF HTML (experimental) Abstract: We apply conformal prediction techniques to regression problems with circular responses, producing prediction sets with adaptive arc length and finite-sample coverage guarantees for any circular predictive model under the assumption of data exchangeability. Leveraging the high performance of existing predictive models designed for linear responses, we analyze a general projection procedure that converts any linear-response regression model into one suitable for circular responses. When random forests are used as base models in this projection procedure, we leverage the random forest out-of-bag mechanism to eliminate the need for a separate calibration sample in the construction of prediction sets. On synthetic and real datasets, the resulting projected random forest model produces more efficient out-of-bag conformal prediction sets, with shorter median arc length, than the split conformal prediction sets generated by two existing alternative models. Comments: 7 pages; 4 figures Subjects: Machine Learning (stat.ML) ; Machine Learning (cs.LG); Methodology (stat.ME) Cite as: arXiv:2410.24145 [stat.ML] (or arXiv:2410.24145v3 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2410.24145 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Knowledge-Based Systems 351, 116772 (2026) Related DOI : https://doi.org/10.1016/j.knosys.2026.116772 Focus to learn more DOI(s) linking to related resources Submission history From: Paulo C. Marques F. [ view email ] [v1] Thu, 31 Oct 2024 17:05:52 UTC (202 KB) [v2] Thu, 26 Dec 2024 04:11:19 UTC (202 KB) [v3] Tue, 9 Jun 2026 19:53:34 UTC (330 KB) Full-text links: Access Paper: View a PDF of the paper titled Projected random forests and conformal prediction of circular data, by Paulo C. Marques F. and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ML < prev | next > new | recent | 2024-10 Change to browse by: cs cs.LG stat stat.ME References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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